Meiotic silencing by unpaired DNA (MSUD) is a process that detects unpaired regions between homologous chromosomes and silences them for the duration of sexual development. While the phenomenon of MSUD is well recognized, the process that detects unpaired DNA is poorly understood. In this report, we provide two lines of evidence linking unpaired DNA detection to a physical search for DNA homology. First, we have found that a putative SNF2-family protein (SAD-6) is required for efficient MSUD in Neurospora crassa. SAD-6 is closely related to Rad54, a protein known to facilitate key steps in the repair of double-strand breaks by homologous recombination. Second, we have successfully masked unpaired DNA by placing identical transgenes at slightly different locations on homologous chromosomes. This masking falls apart when the distance between the transgenes is increased. We propose a model where unpaired DNA detection during MSUD is achieved through a spatially constrained search for DNA homology. The identity of SAD-6 as a Rad54 paralog suggests that this process may be similar to the searching mechanism used during homologous recombination. MEIOSIS is fundamental to sexual reproduction. During meiosis, chromosomes are replicated, aligned, recombined, and segregated to nuclei that will develop into gametes. Two of these key processes, alignment and recombination, likely require a search for DNA homology between chromosomes (Barzel and Kupiec 2008;Moore and Shaw 2009). Such homology searching is necessary because sexual organisms inherit a copy of each chromosome from each of its parents. These chromosomes, referred to as homologs, must somehow find each other so that alignment, recombination, and segregation can occur.Although recent research has improved our understanding of homology search mechanisms (Forget and Kowalczykowski 2012;Renkawitz et al. 2013), there are many questions that remain unanswered. The filamentous fungus Neurospora crassa may be useful for investigating the unknowns of homology searching because it possesses a genetically tractable phenomenon called meiotic silencing by unpaired DNA (MSUD) (Aramayo and Selker 2013;Billmyre et al. 2013). MSUD scans pairs of homologs for segments of DNA that are not accurately paired between them. If improper pairing (i.e., unpairing) is identified, the offending sequences are silenced for the duration of sexual development. For example, if a hypothetical gene called "gene A" is on the left arm of one chromosome but on the right arm of its homolog, it will be silenced. The same holds true if gene A has been lost from one of the homologs.A functional MSUD response can be easily detected with alleles that affect ascospore (sexual spore) color or shape. Indeed, MSUD was discovered during studies of ascospore maturation-1 (asm-1), a gene required for the production of pigmented (black) ascospores . A cross between an asm-1 + strain and an asm-1 D strain produces mostly unpigmented (white) ascospores. This is because MSUD silences the unpaired asm-1 + all...

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Purpose To investigate (1) all‐payer inpatient volume changes at rural hospitals and (2) whether trends in inpatient volume differ by organizational and geographic characteristics of the hospital and characteristics of the patient population. Methods We used a retrospective, longitudinal study design. Our study sample consisted of rural hospitals between 2011 and 2017. Inpatient volume was measured as inpatient average daily census (ADC). Additional measured hospital characteristics included census region, Medicare payment type, ownership type, number of beds, local competition, total margin, and whether the hospital was located in a Medicaid expansion state. Measured characteristics of the local patient population included total population size, percent of population aged 65 years or older, and percent of population in poverty. To identify predictors of inpatient volume trends, we fit a linear multiple regression model using generalized estimating equations. Findings Rural hospitals experienced an average change in ADC of −13% between 2011 and 2017. We found that hospital characteristics (eg, census region, Medicare payment type, ownership type, total margin, whether the hospital was located in a Medicaid expansion state) and patient population characteristics (eg, percent of population in poverty) were significant predictors of inpatient volume trends. Conclusions Trends in inpatient volume differ by organizational and geographic characteristics of the hospital and characteristics of the patient population. Researchers and policy makers should continue to explore the causal mechanisms of inpatient volume decline and its role in the financial viability of rural hospitals.

Objective: To provide an updated analysis of the economic effects of rural hospital closures.Study Setting: Our study sample was national in scope and consisted of nonmetro counties from 2001 to 2018. Study Design:We used a difference-in-differences study design to estimate the effect of a hospital closure on county income, population, unemployment, and size of the labor force. Specifically, we compared economic changes over time in nonmetro counties experiencing a hospital closure to changes in a control group of nonmetro counties over the same time period. We also leveraged insight from recent research to control for estimation bias due to heterogeneity in the closure effect over time or across groups defined by when closure was experienced.Data Extraction: Data on (adjusted gross) annual income (in real dollars), annual population size, and monthly unemployment rate and labor force size were sourced from the Internal Revenue Service, Census Bureau, and Bureau of Labor Statistics, respectively. We used data from the North Carolina Rural Health Research Program to identify counties that experienced a hospital closure.Principal Findings: Of the 1759 nonmetro counties in our study sample, 109 experienced a hospital closure during the study period. Relative to the nonclosure counterfactual, closures significantly decreased labor force size, on average, by 1.4% (95% CI: [À2.1%, À0.8%]). Results also suggest that Prospective Payment System (PPS) hospital closures significantly decreased population size, on average, by 1.1% (95% CI: [À1.7%, À0.5%]), relative to the nonclosure counterfactual.Conclusions: Our analysis suggests that rural hospital closures often have adverse effects on local economic outcomes. Importantly, the negative economic effects of closure appear to be strongest following Prospective Payment System hospital closures and attenuated when the closed hospital is converted to another type of health care facility, allowing for the continued provision of services other than inpatient care.

The purpose of this study was to identify individual and residency program factors associated with increased suicide risk, as measured by suicidal ideation. We utilized a prospective, longitudinal cohort study design to assess the prevalence and predictors of suicidal ideation in 6,691 (2012–2014 cohorts, training data set) and 4,904 (2015 cohort, test data set) first-year training physicians (interns) at hospital systems across the United States. We assessed suicidal ideation two months before internship and then quarterly through intern year. The prevalence of reported suicidal ideation in the study population increased from 3.0% at baseline to a mean of 6.9% during internship. 16.4% of interns reported suicidal ideation at least once during their internship. In the training dataset, a series of baseline demographic (male gender) and psychological factors (high neuroticism, depressive symptoms and suicidal ideation) were associated with increased risk of suicidal ideation during internship. Further, prior quarter psychiatric symptoms (depressive symptoms and suicidal ideation) and concurrent work-related factors (increase in self-reported work hours and medical errors) were associated with increased risk of suicidal ideation. A model derived from the training dataset had a predicted area under the Receiver Operating Characteristic curve (AUC) of 0.83 in the test dataset. The suicidal ideation risk predictors analyzed in this study can help programs and interns identify those at risk for suicidal ideation before the onset of training. Further, increases in self-reported work hours and environments associated with increased medical errors are potentially modifiable factors for residency programs to target to reduce suicide risk.